Learn how to deploy complex machine learning models on single-board computers, mobile phones, and microcontrollers.
Main Features
● Comprehensive understanding of the core concepts of TinyML.
● Learn how to design your own TinyML applications from scratch.
● Explore cutting-edge models, hardware, and software platforms for developing TinyML.
Description
TinyML is an innovative technology that enables small and resource-constrained edge devices to leverage machine learning capabilities. If you are interested in deploying machine learning models directly on microcontrollers, single-board computers, or mobile phones without relying on continuous cloud connectivity, then this book is an ideal resource.
The book starts with a review of Python, covering basic concepts and common libraries such as NumPy and Pandas. It then delves into the fundamentals of neural networks and discusses practical implementations of deep learning using TensorFlow and Keras. Additionally, the book provides detailed coverage of TensorFlow Lite, a framework specifically designed for optimizing and deploying models on edge devices. It also discusses various model optimization techniques that can reduce model size without sacrificing performance. As the book progresses, it offers step-by-step guidance on creating deep learning models for object detection and face recognition in a Raspberry Pi-specific environment. You will also learn about the complexities of deploying TensorFlow Lite applications on real-world edge devices. Finally, the book explores the exciting possibilities of using TensorFlow Lite on microcontroller units (MCUs), opening up new opportunities for deploying machine learning models on resource-constrained devices.
Overall, this book is a valuable resource for anyone interested in harnessing machine learning capabilities on edge devices.
What You Will Learn
● Explore different hardware and software platforms for designing TinyML.
● Create deep learning models for object detection using the MobileNet architecture.
● Optimize large neural network models using the TensorFlow model optimization toolkit.
● Explore the capabilities of TensorFlow Lite on microcontrollers.
● Build a face recognition system on Raspberry Pi.
● Build a keyword detection system on Arduino Nano.
Target Audience
This book is suitable for undergraduate and graduate students in fields such as computer science, artificial intelligence, electronics, and electrical engineering, including MSc and MCA programs. It is also a valuable reference for young professionals who are new to the industry and wish to enhance their skills.
Table of Contents
1. Introduction to TinyML and its Applications
2. Basics of Python and TensorFlow
3. Fundamentals of Deep Learning
4. Experiencing TensorFlow
5. Model Optimization with TensorFlow
6. Deploying My First TinyML Application
7. In-depth Discussion on Application Deployment
8. TensorFlow Lite on Microcontrollers
9. Keyword Detection on Microcontrollers
10. Conclusion and Further Reading
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